Traffic congestion information in the form of, for example, a traffic congestion index (“TCI”) is widely applied in travel planning and driving guidance (see, e.g., GOOGLE Map, BAIDU Map, etc.).
With the development of telematics and sensor and mobility technologies, millions of connected vehicles will be the reality in the near future. And then, global positioning system (“GPS”) data sourced from various vehicles will be typically used for traffic congestion index calculation in a “mega connected vehicle” era.
Referring now to FIG. 1, shown here is an example of a conventional generation of a traffic congestion index depicted on map 101. The traffic congestion index may be show on dedicated portable navigation devices (one of which is shown here as navigation device 103). Further, the traffic congestion index may be show on smart phones (one of which is shown here as smart phone 105). Further still, the traffic congestion index may be show on television broadcasts (one of which is shown here as television broadcast 107).
Still referring to FIG. 1, it is seen that the traffic congestion index may be generated based upon: (a) traffic data from fixed sensors (shown in this FIG. 1 as element 109); (b) traffic data from mobile sensors (shown in this FIG. 1 as element 111); (c) traffic data from public events (shown in this FIG. 1 as element 113); and/or (d) traffic data from GIS-T applications (shown in this FIG. 1 as element 115).
Referring now to FIG. 2A, shown here is an example of a conventional process moving from data preprocessing (201), to data fusion (203), to application (205). As seen, data received from road sensors is preprocessed using: (a) anomaly detection and filtering; and (2) temporal and spatial correlation and compensation. Also, as seen, data received from mobile vehicle sensors is preprocessed using: (a) anomaly detection and filtering; and (2) trajectory pattern analysis; and (3) road mapping. Of note, this conventional workflow includes a lack of measurement with regard to accuracy of data (that is, data uncertainty). Also, in this conventional workflow the data processing and TCI calculation are handled by a different organization.
With respect now in particular to GPS, it is noted that since the readings of a GPS sensor have positioning errors and sampling errors, the departure of the GPS tracking data from the actual trajectory can hardly be avoided. As a result, the task of GPS data preprocessing (including matching original GPS tracking data to a digital map (that is, “map matching”) while handling exceptions, correcting errors, reducing noise and redundancy) is a prerequisite to calculating TCI. With respect to this map matching, reference is now made to FIG. 2B, where it is seen that car A is driving on certain roads. In this example, original GPS tracking data of location sequence of A, B, C was received. The task of map matching is to infer the actual location sequence—it can be A2, B1, C3 or A1, B2, C3 or A3, B1, C2 . . . and so on. The output of map matching is the most likely location sequence or trajectory (in this example, A1, B2, C3 as shown in the darker polyline marked “1”).
However, conventional solutions of TCI calculation typically lack a measurement relating to such uncertainties. That is, conventional solutions of TCI calculation are typically divided into two independent processes: data preprocessing and then TCI calculation. During the conventional TCI calculation phase, all inputs are assumed to be equally certain.
Thus, various embodiments provide a mechanism to measure uncertainties in GPS data processing, and then to improve the quality of traffic congestion index calculation during the second phase. Further, in various embodiments, such a mechanism may be implemented via systems, methods and/or computer program products.